Variational decomposition autoencoding improves disentanglement of latent representations

2026-01-11

arXiv.org

AI Summary

This work addresses the challenge of learning disentangled and interpretable latent representations from complex, non-stationary, high-dimensional time-varying signals, which exhibit rich time-frequency structures that conventional variational autoencoders (VAEs) struggle to model effectively. To this end, we propose the Decompositional Variational Autoencoder (DecVAE), which uniquely integrates signal decomposition priors directly into the VAE framework. DecVAE employs an encoder-only architecture and jointly leverages signal decomposition models, contrastive self-supervised tasks, and variational inference to learn multi-subspace latent representations aligned with the intrinsic time-frequency characteristics of the data. Extensive experiments on synthetic data and three scientific datasets demonstrate that DecVAE substantially outperforms existing VAE approaches, achieving significant improvements in disentanglement quality, cross-task generalization, and interpretability of the learned latent representations.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech and biomedical signal processing, the ability to learn disentangled and interpretable representations is critical for uncovering latent generative mechanisms. Traditional approaches to unsupervised representation learning, including variational autoencoders (VAEs), often struggle to capture the temporal and spectral diversity inherent in such data. Here we introduce variational decomposition autoencoding (VDA), a framework that extends VAEs by incorporating a strong structural bias toward signal decomposition. VDA is instantiated through variational decomposition autoencoders (DecVAEs), i.e., encoder-only neural networks that combine a signal decomposition model, a contrastive self-supervised task, and variational prior approximation to learn multiple latent subspaces aligned with time-frequency characteristics. We demonstrate the effectiveness of DecVAEs on simulated data and three publicly available scientific datasets, spanning speech recognition, dysarthria severity evaluation, and emotional speech classification. Our results demonstrate that DecVAEs surpass state-of-the-art VAE-based methods in terms of disentanglement quality, generalization across tasks, and the interpretability of latent encodings. These findings suggest that decomposition-aware architectures can serve as robust tools for extracting structured representations from dynamic signals, with potential applications in clinical diagnostics, human-computer interaction, and adaptive neurotechnologies.

Problem

• disentangled representation
• nonstationary signals
• time-evolving signals
• latent generative mechanisms
• high-dimensional data

Innovation

• variational decomposition autoencoding
• disentangled representation
• signal decomposition
• contrastive self-supervision
• time-frequency modeling